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Particle Swarm Optimization for Nano-Particles Extraction from Supporting Materials
Mohamed abd-ElRahman Abdou
Pages - 361 - 370     |    Revised - 01-07-2011     |    Published - 05-08-2011
Volume - 5   Issue - 3    |    Publication Date - July / August 2011  Table of Contents
TEM Image Scaning, Particle Swarm Optimization, Image Segmentation, Nano-particles Characterization
Metallic and non-metallic nano-particles have attracted much interest concerning their wide applications. Transmission electron microscopy (TEM) is the state of the art method to characterize a nano-particle with respect to size, morphology, structure, or composition. This paper presents an efficient evolutionary computational method, particle swarm optimization (PSO), for automatic segmentation of nano-particles. A threshold-based segmentation technique is applied, where image entropy is attacked as a minimization problem to specify local and global thresholds. We are concerned with reducing wrong characterization of nano-particles due to concentration of liquid solutions or supporting material within the acquired image. The obtained results are compared with manual techniques and with previous researches in this area.
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Dr. Mohamed abd-ElRahman Abdou
Informatics Research Institute - Egypt